Bioregion, Biopolitics, and the Creaturely List: The Trouble with FaunaWatch
Bibliographic record
Abstract
Canada’s tradition of nature poets who are also philosophically astute (or, conversely, philosophical poets who are astute about bioregionality) is long and would include Don McKay, Tim Lilburn, and Jan Zwicky, to name just a few. My own practice of observing and archiving animals, and writing about such archiving practices, an ongoing project called FaunaWatch, has made it clear that nothing about doing so is simple, just as nothing about being the owner-operator of a fleshy body is simple. This essay examines my practice of observation and archiving a bioregional creaturely list as an important critical and creative process, though one that is powered by an acquisitive energy, raising questions about the culture of sighting and “collecting” sights. FaunaWatch, as practice and as project, has increased in complexity precisely because of its humble (and humbling) beginnings, growing as it did out of my intense desire to fix myself in the realities of my geographical location in southwestern Ontario. When a hybrid of scholarly discourse and bioregional presence goes into the woods, it is no real surprise to find the organic impulse of the poem and the biological organism, the animal self and the animal other, undermined by uncertainty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.049 | 0.080 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".